In predictive microbiology, primary and secondary models can be used to predict microbial growth, usually in a two-step modelling approach. The inverse dynamic modelling approach is an alternative method to direct modelling methods, in which the primary and secondary models are fitted simultaneously from non-isothermal data, minimising experimental effort and costs. Thus, the main aim of the present study was to compare the prediction capabilities of the mathematical modelling approaches used for calculating growth kinetics of microorganisms in predictive food microbiology field. For this purpose, the bacterial growth data of Pseudomonas spp. in oyster mushroom (Pleurotus ostreatus) subjected to isothermal and non-isothermal storage temperatures were collected from previously published growth curves. Temperature-dependent kinetic growth parameters (maximum specific growth rate 'µmax' and lag phase duration 'λ') were described as a function of storage temperature using the direct two-step, direct one-step and inverse dynamic modelling approach based on Baranyi and Huang models. The fitting capability of the modelling approaches was separately compared, and the one-step modelling approach for the direct methods provided better goodness of fit results regardless of used primary models, which leads the Huang model with being RMSE = 0.226 and R2adj = 0.949 became best for direct methods. Like seen in direct methods, the Huang model gave better goodness of fit results than Baranyi model for inverse method. Results revealed there was no significant difference (p > 0.05) between the growth kinetic parameters obtained from direct one-step modelling approach and inverse modelling approaches based on the Huang model. Satisfactorily statistical indexes show that the inverse dynamic modelling approach can be reliably used as an alternative way of describing the growth behaviour of Pseudomonas spp. in oyster mushroom in a fast and minimum labour effort.
Keywords: Inverse dynamic modelling; growth kinetics; mushroom; predictive microbiology.